FsTsetlin 0.4.7

FsTsetlin

Implements a Tsetlin machine learning system in F#. The key difference between this and other Testlin machine implementations is that this library uses tensor operations to parallelize learning and prediction. FsTsetlin utilizes the tensor library underpinning TorchSharp/PyTorch. The libary has been tested to work on both CPU and GPU (although extensive performance testing has not been performed as of yet)

Note: This is still a work-in-progress. Please treat it as beta-level code.

Tsetline Machine

Tsetlin machine (TM) is a recently developed machine learning system based on automata (finite state machines / propositional logic) learning. Please see this paper for details.

TM is said to be competitive on many tasks with other ML methods (both DL & classical). However, the main draw is that TM-based models are faster to train and more energy-efficient when used for inferencing than models based on other ML methods - while providing similar accuracy. As ML becomes pervasive, the compute and power costs of deployed models becomes non-trivial. TM may help to reign in runtime and training costs associated with ML at scale.

Although this implementation is in F#, the goal is to define a largely language-agnostic computation approach that can be easily ported to other languages (e.g. Python, Java, etc.), as long as the language has a binding to libtorch - the C++ tensor library underlying PyTorch.

There are other GPU implementations available (see github repo). None of these use tensor operations from a standard tensor library. By using PyTorch as an established standard, the desire is to gain wider deployment portability. For example, the TM may be trained on a GPU but later deployed to a CPU-based environment for inferencing, to save costs.

Examples

• NoisyXor.fsx (binary)
• BinaryIris.fsx (multiclass)

Datasets courtesy of https://github.com/cair/TsetlinMachine